Data Viz Project - Terrorism
Group 13
How, When and Where Do Terrorists Attack?
library(readr)
terror <- read_csv("ussubset.csv")
# Bar graph for types of attacks
library(ggplot2)
library(ggthemes)
library(dplyr)
terror$Ideology <- terror$DOM_I
terror <- terror %>%
group_by(attacktype1) %>%
mutate(total = n())
terror1 <- arrange(terror, total)
terror1$attacktype1 <- factor(terror1$attacktype1, levels = rev(unique(terror1$attacktype1)))
gg_type <- ggplot(terror, aes(x = attacktype1_txt, fill = Ideology)) +
geom_bar(width = 0.5) +
geom_text(aes(x = attacktype1_txt, y = total, label = total),
hjust = -0.1,
size = 3.5,
check_overlap = TRUE) +
expand_limits(y = c(0, 120)) +
coord_flip() +
scale_fill_manual(values = c("purple", "red", "darkgreen", "blue", "orange", "grey")) +
theme_economist_white(base_size = 8) +
xlab("Type of Attack") +
ylab("Number of Attacks") +
labs(title = "Attack Count for the Different Types of Attacks")
gg_type
Armed Assualts are the most common form of terrorist attacks, followed by assassination and then bombing.
For armed assault attacks, a large majority of the terrorism groups adopt the left-wing and right-wing ideologies.
library(plotly)
terror2 <- terror %>%
group_by(iyear, Ideology) %>%
mutate(total = n(),
nkills = sum(nkill))
terror_left <- filter(terror2, Ideology == "Left-Wing")
terror_right <- filter(terror2, Ideology == "Right-Wing")
plot1 <- ggplot(terror2, aes(x = iyear,y = total,
label = nkills)) +
geom_line(aes(colour = Ideology), na.rm = TRUE) +
ylab("Number of Attacks each year") +
labs(title = "Number of Attacks over time") +
theme_economist_white() +
theme(axis.text.x = element_text(size = 7.5))
ggplotly(plot1)# Highlighting only the left and right wing ideologies so that it is less cluttered.
g <- terror2 %>% group_by(Ideology) %>%
plot_ly() %>% layout(title = "Line graph for Number of Attacks over time",
xaxis = list(title = "Year"),
yaxis = list(title = "Number of Attacks")) %>%
add_lines(x = terror2$iyear, y = terror2$total,
alpha = 0.2, name = "Other Ideologies",
hoverinfo = 'text',
text = ~paste('</br> Year: ', iyear,
'</br> Number of Attacks: ', total,
'</br> Number of Fatalities: ', nkills,
'</br> Ideology: ', Ideology),
type = "scatter", mode = "lines",
line = list(color = 'rgba(192,192,192,0.4)'))
g %>% add_lines(x = terror_left$iyear, y = terror_left$total,
name = "Left-Wing",
hoverinfo = 'text',
text = ~paste('</br> Year: ', terror_left$iyear,
'</br> Number of Attacks: ', terror_left$total,
'</br> Number of Fatalities: ', terror_left$nkills,
'</br> Ideology: ', terror_left$Ideology),
type = "scatter", mode = "lines", line = list(color = c('red'))) %>%
add_lines(x = terror_right$iyear, y = terror_right$total,
name = "Right-Wing",
hoverinfo = 'text',
text = ~paste('</br> Year: ', terror_right$iyear,
'</br> Number of Attacks: ', terror_right$total,
'</br> Number of Fatalities: ', terror_right$nkills,
'</br> Ideology: ', terror_right$Ideology), type = "scatter",
mode = "lines", line = list(color = c('blue'))) The number of terror attacks fluctuate erratically across the years, though there seem to be a general decrease in number of attacks.
Terrorist groups adopting the left-wing ideologies were the ones who conducted the most number of terrorist attacks from 1970 to 1973. The worst year would be 1970 where the left-wing terrorist groups were involved in 17 attacks which lead to 19 fatalities. However, the number of attacks decreased subsequently and there has not been any attacks organised by left-wing terrorist groups from 1981 onwards.
On the other hand, the terrorist groups with right-wing ideologies were involved in a moderate number of terror attacks and 7 was the highest number of attacks which they have been involved in the year 1980. But they have still been active in causing terror attacks across the years.
library(rgdal)
library(leaflet)
library(dplyr)
library(plyr)
library(magrittr)
library(RColorBrewer)
library(stringr)
library(ggplot2)
library(ggthemes)
us_fatal <- read.csv("ussubset.csv")
usa <- readOGR('cb_2016_us_state_20m.shp', verbose = FALSE)
us_fatal <- us_fatal[us_fatal$latitude >= 24.7433195,]
us_fatal <- us_fatal[us_fatal$latitude <= 49.3457868,]
us_fatal <- us_fatal[us_fatal$longitude >= -124.7844079,]
us_fatal <- us_fatal[us_fatal$longitude <= -66.9513812,]
us_fatal_2010 <- subset(us_fatal, attackdecade==2010)
us_fatal_2000 <- subset(us_fatal, attackdecade==2000)
us_fatal_1990 <- subset(us_fatal, attackdecade==1990)
us_fatal_1980 <- subset(us_fatal, attackdecade==1980)
us_fatal_1970 <- subset(us_fatal, attackdecade==1970)
pal = colorFactor(c("purple","red","green","blue","orange","black"), domain = us_fatal$DOM_I)
color_ideology_2010 = pal(us_fatal_2010$DOM_I)
color_ideology_2000 = pal(us_fatal_2000$DOM_I)
color_ideology_1990 = pal(us_fatal_1990$DOM_I)
color_ideology_1980 = pal(us_fatal_1980$DOM_I)
color_ideology_1970 = pal(us_fatal_1970$DOM_I)
leaflet(usa, width ="100%", height = "400px" ) %>% setView(lat=33, lng=-103 , zoom=3.5) %>% addPolygons(stroke = TRUE, smoothFactor = 0.5, weight=1, color='#333333', opacity=0.5, fillColor = "white", label = usa@data$NAME) %>%
addCircleMarkers(group="2010 attacks", data=us_fatal_2010, lng = ~longitude, lat = ~latitude, color=color_ideology_2010, fillColor=color_ideology_2010, radius=1.5*sqrt(us_fatal_2010$nkill), weight=1, opacity = 1, popup = paste("Perpetrator:",us_fatal_2010$gname,"<br/>", "State:",us_fatal_2010$provstate,"<br/>", "Date of Attack:", us_fatal_2010$date,"<br/>", "Ideology:", us_fatal_2010$DOM_I,"<br/>", "Number of Fatalities:", us_fatal_2010$nkill, "<br/>", "Attack Type:", us_fatal_2010$attacktype1_txt, "<br/>")) %>%
addCircleMarkers(group="2000 attacks", data=us_fatal_2000, lng = ~longitude, lat = ~latitude, color=color_ideology_2000, fillColor=color_ideology_2000, radius=1.5*sqrt(us_fatal_2000$nkill), weight=1, opacity = 1, popup = paste("Perpetrator:",us_fatal_2000$gname,"<br/>","State:", us_fatal_2000$provstate,"<br/>","Date of Attack:", us_fatal_2000$date,"<br/>", "Ideology:", us_fatal_2000$DOM_I,"<br/>", "Number of Fatalities:", us_fatal_2000$nkill, "<br/>", "Attack Type:", us_fatal_2000$attacktype1_txt, "<br/>")) %>%
addCircleMarkers(group="1990 attacks", data=us_fatal_1990, lng = ~longitude, lat = ~latitude, color=color_ideology_1990, fillColor=color_ideology_1990, radius=1.5*sqrt(us_fatal_1990$nkill), weight=1, opacity = 1, popup = paste("Perpetrator:",us_fatal_1990$gname,"<br/>","State:", us_fatal_1990$provstate,"<br/>", "Date of Attack:", us_fatal_1990$date,"<br/>", "Ideology:", us_fatal_1990$DOM_I,"<br/>", "Number of Fatalities:", us_fatal_1990$nkill, "<br/>", "Attack Type:", us_fatal_1990$attacktype1_txt, "<br/>")) %>%
addCircleMarkers(group="1980 attacks", data=us_fatal_1980, lng = ~longitude, lat = ~latitude, color=color_ideology_1980, fillColor=color_ideology_1980, radius=1.5*sqrt(us_fatal_1980$nkill), weight=1, opacity = 1, popup = paste("Perpetrator:",us_fatal_1980$gname,"State:", us_fatal_1980$provstate,"<br/>","<br/>","Date of Attack:", us_fatal_1980$date,"<br/>", "Ideology:", us_fatal_1980$DOM_I,"<br/>", "Number of Fatalities:", us_fatal_1980$nkill, "<br/>", "Attack Type:", us_fatal_1980$attacktype1_txt, "<br/>")) %>%
addCircleMarkers(group="1970 attacks", data=us_fatal_1970, lng = ~longitude, lat = ~latitude, color=color_ideology_1970, fillColor=color_ideology_1970, radius=1.5*sqrt(us_fatal_1970$nkill), weight=1, opacity = 1, popup = paste("Perpetrator:",us_fatal_1970$gname,"<br/>","State:", us_fatal_1970$provstate,"<br/>","Date of Attack:", us_fatal_1970$date,"<br/>", "Ideology:", us_fatal_1970$DOM_I,"<br/>", "Number of Fatalities:", us_fatal_1970$nkill, "<br/>", "Attack Type:", us_fatal_1970$attacktype1_txt, "<br/>")) %>%
addLegend(pal = pal, values = ~us_fatal$DOM_I, title = "Ideology of Perpetrator", position = "bottomright") %>%
addLayersControl(
overlayGroups = c("2010 attacks","2000 attacks", "1990 attacks", "1980 attacks", "1970 attacks"),
options = layersControlOptions(collapsed = TRUE)) %>% hideGroup("2000 attacks") %>% hideGroup("1990 attacks") %>% hideGroup("1980 attacks") %>% hideGroup("1970 attacks")To build on the earlier visualizations of summary statistics of the terrorist attacks, the leaflet map above shows the location of the attacks across the years (different layers). The locations are color-coded according to the ideology of the perpetrator as shown in the legend, and sized according to the number of fatilities. Specifics of the attacks including information on the perpetrator, state, date of attack, and type of attack are detailed in pop-ups when the markers are clicked.
The following are some noteworthy trends from this visualization. Firstly, the number of attacks seem to be decreasing over time as later decades have fewer markers on the map. However, the number of fatilites stemming from each attack tend to be larger for the later attacks as indicated by the larger radius of the markers for the layers corresponding to later decades. Secondly, most of the attacks tend to be driven by religious or right-wing ideologies since most of the markers are either blue or green. Thirdly, attacks occur more frequently in the east coast area around New York and the west coast area around California. The notorious 9/11 attack stands out in this visualization with the large green circle marker in the layer corresponding to 2000-2010 attacks.
From the aforementioned takeaways from the visualization, we can infer that security has probably improved in terms of reducing the number of attacks but concurrently, terrorists seem to be going for more major attacks that will result in more fatalities. Moreover, we should be wary of conflicts relating to religious or right-wing ideologies as those seem to be prone to result in a terrorist attack. Finally, the coastal areas should be more wary as they are more likely targets of a terrorist attack.
State Level Maps - Are Some States More Vulnerable Than Others
Investigating potential trends and differences in attacks by state across time, the heatmap above shows the frequency of attacks for each state across the decades. From the heatmap, attacks appear to be most widespread across states in the 1970s and 2010s, with most states having experienced at least one attack in these two decades. On the other hand, a majority of the states did not experience any attack in the 1990s and 2000s, although a higher concentration of attacks in states like New York, District of Columbia and Florida. This may be indicative of a change in trend from having a widespread attack (attacking most states) in earlier years to more focused attacks (on few states), but a reversal to more widespread attacks in the last decade.
Analysing trends in frequency of attacks for each state, certain states like California, New York and Texas have experienced attacks across all decades, with the highest number of attacks for California and New York especially in the 1970s. This may be indicative of more focused plans in targeting particular states such as these, where these states tend to have the largest population sizes and hence more frequently targeted (potentially to aim for a bigger impact).
Moving from the broad picture of the details of the terrorist attacks in US as a whole to the specific picture of the relationship between each state and the attacks, we now find a possible reason as to why California and New York are prone to terrorist attacks. Those are two states with very high GDP per capita as portrayed by the Choropleth shading and terrorist groups may think that attacking those states would result in a greater disruption of (economic) activity. As a corollary, it is also worth noting that states with lower GDP per capita (lighter shades) tend to be less affected by terrorist attacks.
Word Cloud - What Drives Terrorist Organizations
Moving on from the factors influencing statesâ susceptibility to attacks, we now investigate what drives the different terrorism groups to operate and identify similarities and differences in their missions.
The founding philosophies of terrorism groups describe the groupâs mission and epistemological concerns. To analyse the similarities and differences in missions across the 5 dominant ideologies, the word clouds here show the most frequent terms used in describing the founding philosophies of different ideologies, colour coded by ideology type.
Comparing the terms used in the founding philosophies by ideology, there appears to be a common theme of âraceâ and âreligionâ driving ideologies, while each taking a different stance. This may point to a common trend of terrorism groups sparked by racial discontent or religion, especially rampant given the high frequency of race and religion-affiliated words in most of the word clouds.
Firstly, Right-Wing and Left-Wing groups tend to be mostly driven by racial missions, with the theme of âraceâ most common in these 2 ideologies. Unsurprisingly, there are apparent differences in their racial stances, with Right-Wing groups focusing on âwhiteâ, and Left-Wing groups focusing on âblackâ.
Next, the common theme of religion is more apparent amongst the Right-Wing, Religious and Ethno-nationalist groups, but again with different stances. While Right-Wing groups have missions geared towards Christianity with top words like âchristianâ, Religious groups tend to have missions focusing on âislamâ, and Ethno-nationalist groups on âjewishâ.
In addition, examining other themes, the Left-Wing and Ethno-nationalist ideologies appear to have similar philosophies driven by missions for freedom with words like âstruggleâ, âoppressedâ and âindependenceâ. This may provide basis for potential connections between groups under these 2 ideologies, given some overlaps of similar philosophies. On the other hand, the Right-Wing groups have more âpoliticalâ and âfederalâ concerns, and may thus be more independent in their missions overall.
Lastly, the Single Issue groups appear to be driven by a wide spectrum of concerns, with similar concerns with the other ideologies like âgovernmentâ and âliberationâ, but also entirely different issues such as âanimalâ, âcubanâ and âenvironmentâ.
Overall, these similarities and differences in ideologies may provide basis for connections between certain terrorism groups and in turn, influence how they operate.
Network Visualization - How Do They Interact
In the network visualisation, the colour of the nodes represent the ideology of a terrorist organisation, the size of the node represents the number of years the organisation has been active for, and the line type of each edge represents whether an edge is between nodes who subscribe to the same or different ideologies.
From the visualisation, we find terrorist networks are organised into one large interconnected component and five smaller disparate components. The five smaller disparate components are more or less homogenous by ideology and in the large interconnected component, terrorist organisations with the same ideology cluster together,
From the names of the terrorist organisations in the five smaller disparate components, we can roughly tell that they comprise a far-right White Nationalist group, a fundamentalist Islamic group, a Cuban ethno-nationalist group, a Zionist group and an anti-communist group.
In the large inter-connected node, we also see four distinct groups. The first group comprises a Puerto-Rican ethno-nationalist group; the second comprises a far-left group; the third comprises foreign militant organisations, and the fourth comprises environmental groups.
The visualisation also reveal that the longest-surviving organisations such as Macheteros and the Black Liberation Army are most central to their groups and the larger network.